Model Selection in Bayesian Neural Networks via Horseshoe Priors

29 May 2017

Finale Doshi-Velez

Papers citing "Model Selection in Bayesian Neural Networks via Horseshoe Priors"

25 / 25 papers shown

Title
Bayesian Computation in Deep Learning Wenlong Chen Bolian Li Ruqi Zhang Yingzhen Li BDL 75 0 0 25 Feb 2025
Posterior and variational inference for deep neural networks with heavy-tailed weights Ismael Castillo Paul Egels BDL 60 4 0 05 Jun 2024
FineMorphs: Affine-diffeomorphic sequences for regression Michele Lohr L. Younes 29 0 0 26 May 2023
Masked Bayesian Neural Networks : Theoretical Guarantee and its Posterior Inference Insung Kong Dongyoon Yang Jongjin Lee Ilsang Ohn Gyuseung Baek Yongdai Kim BDL 34 4 0 24 May 2023
Interpretable (not just posthoc-explainable) heterogeneous survivor bias-corrected treatment effects for assignment of postdischarge interventions to prevent readmissions Hongjing Xia Joshua C. Chang S. Nowak Sonya Mahajan R. Mahajan Ted L. Chang Carson C. Chow 38 1 0 19 Apr 2023
Post-hoc Uncertainty Learning using a Dirichlet Meta-Model Maohao Shen Yuheng Bu P. Sattigeri S. Ghosh Subhro Das G. Wornell UQCV OOD BDL 15 31 0 14 Dec 2022
Efficient variational approximations for state space models Rubén Loaiza-Maya D. Nibbering 11 1 0 20 Oct 2022
Variational Inference for Infinitely Deep Neural Networks Achille Nazaret David M. Blei BDL 27 11 0 21 Sep 2022
Interpretable (not just posthoc-explainable) medical claims modeling for discharge placement to prevent avoidable all-cause readmissions or death Joshua C. Chang Ted L. Chang Carson C. Chow R. Mahajan Sonya Mahajan Joe Maisog Shashaank Vattikuti Hongjing Xia FAtt OOD 37 0 0 28 Aug 2022
Rethinking Bayesian Learning for Data Analysis: The Art of Prior and Inference in Sparsity-Aware Modeling Lei Cheng Feng Yin Sergios Theodoridis S. Chatzis Tsung-Hui Chang 68 75 0 28 May 2022
Deep neural networks with dependent weights: Gaussian Process mixture limit, heavy tails, sparsity and compressibility Hoileong Lee Fadhel Ayed Paul Jung Juho Lee Hongseok Yang François Caron 48 10 0 17 May 2022
Encoding Domain Knowledge in Multi-view Latent Variable Models: A Bayesian Approach with Structured Sparsity Arber Qoku Florian Buettner 29 5 0 13 Apr 2022
Graph Reparameterizations for Enabling 1000+ Monte Carlo Iterations in Bayesian Deep Neural Networks Jurijs Nazarovs Ronak R. Mehta Vishnu Suresh Lokhande Vikas Singh UQCV BDL OOD 22 5 0 19 Feb 2022
Identifiable Deep Generative Models via Sparse Decoding Gemma E. Moran Dhanya Sridhar Yixin Wang David M. Blei BDL 31 45 0 20 Oct 2021
A Survey of Uncertainty in Deep Neural Networks J. Gawlikowski Cedrique Rovile Njieutcheu Tassi Mohsin Ali Jongseo Lee Matthias Humt ... R. Roscher Muhammad Shahzad Wen Yang R. Bamler Xiaoxiang Zhu BDL UQCV OOD 61 1,111 0 07 Jul 2021
Consistent Sparse Deep Learning: Theory and Computation Y. Sun Qifan Song F. Liang BDL 45 27 0 25 Feb 2021
Model Fusion with Kullback--Leibler Divergence Sebastian Claici Mikhail Yurochkin S. Ghosh Justin Solomon FedML MoMe 26 33 0 13 Jul 2020
Depth Uncertainty in Neural Networks Javier Antorán J. Allingham José Miguel Hernández-Lobato UQCV OOD BDL 43 100 0 15 Jun 2020
Global inducing point variational posteriors for Bayesian neural networks and deep Gaussian processes Sebastian W. Ober Laurence Aitchison BDL 26 60 0 17 May 2020
Interpretable Outcome Prediction with Sparse Bayesian Neural Networks in Intensive Care H. Overweg Anna-Lena Popkes A. Ercole Yingzhen Li José Miguel Hernández-Lobato Yordan Zaykov Cheng Zhang 36 24 0 07 May 2019
Structured Variational Learning of Bayesian Neural Networks with Horseshoe Priors S. Ghosh Jiayu Yao Finale Doshi-Velez BDL UQCV 17 77 0 13 Jun 2018
Scalable Bayesian Learning for State Space Models using Variational Inference with SMC Samplers Marcel Hirt P. Dellaportas BDL 20 10 0 23 May 2018
Posterior Concentration for Sparse Deep Learning Nicholas G. Polson Veronika Rockova UQCV BDL 30 88 0 24 Mar 2018
Interpretable VAEs for nonlinear group factor analysis Samuel K. Ainsworth N. Foti Adrian K. C. Lee E. Fox OOD DRL 21 19 0 17 Feb 2018
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning Y. Gal Zoubin Ghahramani UQCV BDL 287 9,156 0 06 Jun 2015